Data-Driven Customer Behavior Analysis and Personalized Marketing Strategies

Abstract

In the rapidly evolving e-commerce landscape, personalized recommendation systems have become crucial for enhancing the customer shopping experience and boosting sales conversions. Traditional recommendation systems, particularly those based on collaborative filtering, have been successful in understanding user preferences. However, they often overlook the impact of temporal dynamics, which are essential in the ever-changing e-commerce market. Integrating deep learning-based time series analysis, such as Gated Recurrent Units (GRUs), with collaborative filtering presents a novel breakthrough in e-commerce personalization. The primary challenges faced by current recommendation systems include effectively processing and utilizing the temporal evolution of user behavior and accurately capturing both long-term and short-term user preferences. Additionally, maintaining algorithmic efficiency and accuracy in large datasets, as well as addressing the "cold start" problem for new users or products, are key challenges that need to be addressed. This study proposes a method that combines time series analysis with collaborative filtering. Initially, GRUs analyze users' purchase history to capture behavioral patterns and trends over time. Subsequently, a Neural Collaborative Filtering (NCF) model processes user-item interactions, learning complex user preferences and item characteristics. In particular, the model demonstrates substantial improvements in addressing dynamic user behaviors and the “cold start” problem. These achievements not only enhance the user experience on e-commerce platforms but also lead to higher customer retention rates and increased sales revenue, underscoring the significant potential and practical value of deep learning in personalized e-commerce recommendations.